Systems and methods for determining quality metrics of an image or images based on an edge gradient profile and characterizing regions of interest in an image or images

ABSTRACT

Disclosed herein are systems and methods for determining quality metrics of images based on an edge gradient profile and characterizing regions of interest in an image or images. According to an aspect, a method includes using an imaging device to acquire one or more images including at least a portion of an organ of a subject. The method also includes computing an edge profile across an organ interface of a subject. The method also includes computing an edge gradient profile from the edge profile of a subject. The method also includes computing a image quality metric related to the spatial resolution of the image or images from the edge gradient profile. The method also includes defining multiple regions of interest within the portion of the organ. Further, the method includes characterizing the regions of interest based on predetermined criteria. The method also includes presenting the characterization of the edge gradient profile and characterization of the regions of interest to a user.

CROSS REFERENCE TO RELATED APPLICATION

This application claims priority to U.S. Provisional Patent ApplicationNo. 62/412,964, filed Oct. 26, 2016, and titled AUTOMATED METHODS FORMEASURING RESOLUTION AND CONTRAST IN CLINICAL COMPUTED TOMOGRAPY IMAGES,the disclosure of which is incorporated herein by reference in itsentirety.

TECHNICAL FIELD

The presently disclosed subject matter relates to imaging systems. Moreparticularly, the presently disclosed subject matter relates to systemsand methods for determining a quality metric of images based on an edgegradient profile and characterizing regions of interest in images.

BACKGROUND

The utility of computed tomography (CT) in the clinic has been wellestablished as evidenced by the more than 70 million CT exams performedevery year. Consequently, CT has become the largest contributor topopulation doses amongst all medical imaging modalities that utilizeionizing radiation. Acknowledging the fact that ionizing radiation posesa health risk, there exists the need to strike a balance betweendiagnostic benefit and radiation dose. While characterization of dosefor individual patients has received widespread attention and mandate,image quality tracking has not been as well recognized. However, toensure that CT scanners are optimally used in the clinic, anunderstanding and characterization of both image quality and radiationdose are needed. Moreover, CT systems are ever-expanding in terms oftechnology and application. Therefore, delicacy and performance of thesenew advancements in terms of dosimetry and image quality should bequantitatively measured and periodically monitored to ascertain theoverall quality, consistency, and safety.

Image quality assessments are generally addressed using three primaryimage quality metrics: spatial resolution, noise, and image contrast.Currently, these metrics are generally characterized in static phantomsand ascribed to CT systems and protocols. However, the key objective forimage quality assessment should be its quantification in clinicalimages; that is the most clinically-relevant characterization of imagequality as it is most directly related to the actual quality of theclinical image(s). Phantom measurements are relevant, but only to theextent that they reflect attributes of actual clinical images. Forcharacterizing contrast specifically, the general approach is to imbedinserts made of materials with various densities into a uniform phantomand measure the mean voxel value inside each insert. This method issufficient for characterizing contrast for static, uniformly shapedobjects. However, clinical images are subject to a number ofvariabilities associated with patient size, heterogeneity, motion,photon flux, and contrast perfusion that are not reflected inphantom-based measurements. As such, phantom-based measurements are notsufficient to capture all relevant information related to the spatialresolution and image contrast observed clinically. Methods to measurespatial resolution and contrast in clinical images would be most helpfulto quantify image quality in a patient-specific manner. Such a method,in combination with patient-based noise and resolution estimations,could be used to monitor clinical protocols in an effort to quantifyoverall image quality and consistency. Since many images are acquiredevery day in the clinic, the image quality monitoring algorithms shouldbe fully automated, fast, and robust. Currently, there is not a unifiedpackage in clinical operations that automatically measures and monitorsclinical image spatial resolution and contrast. Accordingly, there isneed to provide improved systems and techniques for presenting qualityinformation for CT images in a clinical setting.

SUMMARY

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter.

Disclosed herein are systems and methods for determining quality metricsof images based on an edge gradient profile and characterizing regionsof interest in images. According to an aspect, a method includes usingan imaging device to acquire one or more images of a subject. The methodalso includes defining skin of the subject in the acquired one or moreimages. Further, the method includes characterizing edge sharpnessacross the skin of the subject via edge profile measurements. The methodalso includes grouping the edge profile measurements by a radialdistance of the edge profiles from an isocenter of the imaging device togenerate oversampled edge profile measurements. Further, the methodincludes determining the oversampled edge profiles that are apredetermined amount of in-plane resolution for obtaining an edgegradient profile. The method also includes determining a quality metricof the acquired one or more images based on the edge gradient profile.

According to another aspect, a method includes using an imaging deviceto acquire one or more images including at least a portion of an organof a subject. The method also includes defining multiple regions ofinterest within the portion of the organ. Further, the method includescharacterizing the regions of interest based on predetermined criteria.The method also includes presenting the characterization of the regionsof interest to a user.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing summary, as well as the following detailed description ofvarious embodiments, is better understood when read in conjunction withthe appended drawings. For the purposes of illustration, there is shownin the drawings exemplary embodiments; however, the presently disclosedsubject matter is not limited to the specific methods andinstrumentalities disclosed. A brief description of the drawingsfollows.

FIG. 1 is a schematic diagram of an example CT imaging system fordetermining a quality metric of an acquired CT image or images inaccordance with embodiments of the present disclosure.

FIG. 2 is a flow diagram of an example method for determining a qualitymetric of an acquired CT image or images in accordance with embodimentsof the present disclosure.

FIG. 3 are different images representing a sequence of an example methodfor generating the polyhedron mesh of a patient in accordance withembodiments of the present disclosure.

FIGS. 4A-4E are graphs showing an example method for reconstructing theright tail of the edge profile (embodied as an edge-spread function,ESF) with the left tail.

FIG. 5 is a diagram of an example method for calculating the resolutionindex (RI) from making an oversampled ESF measurement across thepatient's skin.

FIG. 6 is an example picture of the graphical user interface created forthe observer study, as well as six example edge pairs.

FIGS. 7A-7F show graphs of results from f₅₀ measurements made fromclinical datasets.

FIGS. 8A and 8B are plots of f50 measured from patient images versus f50measured from a conventional resolution measurement phantom.

FIG. 9 shows the results from the observer study.

FIG. 10 is a flow diagram of an example method for determining a qualitymetric of an acquired CT image in accordance with embodiments of thepresent disclosure.

FIGS. 11A-11C are different images depicting a sequence of an examplemethod for segmenting out the patient's body from a CT dataset inaccordance with embodiments of the present disclosure.

FIGS. 12A-12E is a flow diagram of an example method for determining thelung HU histogram in accordance with embodiments of the presentdisclosure.

FIG. 13A-13E are images showing a sequence of a flow diagram of anexample method for determining the liver HU histogram.

FIGS. 14A and 14B are graphs showing example sets of histograms of theHUs for the various organs determined using the automated techniquecompared against the manual technique.

FIGS. 15A and 15B are graphs showing the plots of the metrics determinedfrom the automated technique plotted against those determined from themanual technique for non-contrast enhanced exams.

FIGS. 16A and 16B are graphs showing the plots of the metrics determinedfrom the automated technique plotted against those determined from themanual technique for contrast enhanced exams.

DETAILED DESCRIPTION

The presently disclosed subject matter is described with specificity tomeet statutory requirements. However, the description itself is notintended to limit the scope of this patent. Rather, the inventors havecontemplated that the claimed subject matter might also be embodied inother ways, to include different steps or elements similar to the onesdescribed in this document, in conjunction with other present or futuretechnologies.

Articles “a” and “an” are used herein to refer to one or to more thanone (i.e., at least one) of the grammatical object of the article. Byway of example, “an element” means at least one element and can includemore than one element.

Unless otherwise defined, all technical terms used herein have the samemeaning as commonly understood by one of ordinary skill in the art towhich this disclosure belongs.

As referred to herein, the term “computing device” should be broadlyconstrued. It can include any type of device including hardware,software, firmware, the like, and combinations thereof. A computingdevice may include one or more processors and memory or other suitablenon-transitory, computer readable storage medium having computerreadable program code for implementing methods in accordance withembodiments of the present disclosure. A computing device may be, forexample, a server. In another example, a computing device may be amobile computing device such as, for example, but not limited to, asmart phone, a cell phone, a pager, a personal digital assistant (PDA),a mobile computer with a smart phone client, or the like. A computingdevice can also include any type of conventional computer, for example,a laptop computer, a desktop computer, or a tablet computer.

In accordance with embodiments, automated systems and methods areprovided to measure spatial resolution and image contrast in CT imagesfor monitoring quality of routine image acquisitions as well asquantitatively measuring delicacy and consistency of imaging techniquesin the clinic. As an example, the CT images can be CT chest images. Thesystems and methods may be used on both contrast enhanced andnon-contrast enhanced datasets. An example method can be based onautomatically sampling the lung tissue and liver to measure thehistogram of the Hounsfield units (HUs) inside these organs. A method inaccordance with embodiments was validated against manual measurements incontrast enhanced and non-contrast enhanced clinical chest CT datasets.A second example method can be based on automatically measuring an edgegradient profile across the air/skin interface of the patient. Thismeasurement could be used to extract a quality metric related to thespatial resolution of the image or images through differentiation of theedge gradient profile and Fourier analysis.

FIG. 1 illustrates a schematic diagram of an example CT imaging systemfor determining a quality metric of an acquired CT image in accordancewith embodiments of the present disclosure. Referring to FIG. 1, thesystem includes a rotational gantry 100 that is rotatable about alongitudinal axis of a patient's body 104 or any other object to beexamined. The gantry 100 may include one or more x-ray sources or tubes102 that are configured to project a beam of x-rays towards an x-raydetector array 103 placed at the opposite side of the gantry 100. Thex-ray detector array 103 can be equipped with multiple detector elementswhich can together sense the projected x-rays passing through thepatient's body 104 to be examined between x-ray detector array 103 andx-ray source(s) 102. Each detector element can generate an electricalsignal that represents the intensity of an impinging x-ray beam and canhence be used to estimate the attenuation of the beam as it passesthrough the object.

In a rotational CT scanner such as depicted in FIG. 1, a 3D volume canbe calculated by reconstructing and stacking individual 2D slices. SomeCT imaging systems can employ 2D detector arrays, allowing theacquisition of a truly 3D data sets. As shown, only a single row ofdetector elements is shown (i.e., a detector row). However, amulti-slice detector array may include multiple parallel rows ofdetector elements such that projection data corresponding to multiplequasi-parallel or parallel slices can be acquired simultaneously duringa scan. The detector elements may completely encircle the patient 104.This figure shows only a single x-ray source 102, but it should beunderstood that multiple x-ray sources may be positioned around gantry100.

Operation of x-ray source 102 can be governed by a control mechanism 109of the system. Control mechanism 109 can include an x-ray controller 110that provides power and timing signals to the x-ray source 102. A dataacquisition system (DAS) 111 belonging to the control mechanism 109 cansample analog data from detector elements and can convert the data todigital signals for subsequent processing. An image reconstructor 112can receive sampled and digitized x-ray data from DAS 111 and canperform high-speed image reconstruction. The reconstructed image can beapplied as an input to a computing device 113 (e.g., a desktop or laptopcomputer), which stores the image in a mass storage device 114. Thecomputing device 113 may include hardware, software, firmware, orcombinations thereof for implementing the functionality describedherein. For example, the computing device 113 may include one or moreprocessors 130 and memory 132. The image reconstructor 112 may bespecialized hardware residing in the computing device 113 or a softwareprogram executed by the computing device 113.

The computing device 113 may receive signals via a user interface orgraphical user interface (GUI). Particularly, the computing device 113may receive commands and scanning parameters from a user interface 115that includes, for example, a keyboard and mouse (not shown). Anassociated display 116 can allow an operator to observe thereconstructed image and other data from the computing device 113. Theoperator-supplied commands and parameters can be used by the computingdevice 113 to provide control signals and information to the x-raycontroller 110, DAS 111, and a table motor controller 117 incommunication with a patient table 136, which controls a motorizedpatient table 136 so as to position patient 104 in gantry 101.Particularly, patient table 136 can move the patient 104 through agantry opening.

The computing device 113 may be used for implementing functionalitydescribed herein. Particularly, for example, the computing device 113may include hardware, software, firmware, and combinations thereof forimplementing the methods and techniques disclosed herein. For example,the methods and techniques may be implemented by the processor(s) 130and memory 132 as will be understood by those of skill in the art.Further, a user may suitably interact with the user interface 115 forimplementing the functions and for presenting results to the user.

FIG. 2 illustrates a flow diagram of an example method for determining aquality metric of an acquired CT image in accordance with embodiments ofthe present disclosure. In this example, the method is described asbeing implemented by the system shown in FIG. 1, although it should beunderstood that the method may be implemented by any other suitableimaging system. Alternative systems may be CT imaging systems or non-CTimaging system, such as magnetic resonance imaging (MM).

Referring to FIG. 2, the method includes using 200 an imaging device toacquire one or more images of a subject. For example, the system of FIG.1 may be operated to acquire multiple CT images of a portion of thepatient's body 104. The system may direct x-rays towards and through thebody 104. The detector elements of the x-ray detector array 103 mayreceive the x-rays projected through the body 104. The DAS 111 canreceive analog data from the detector elements and can convert the datato digital signals representative of the body 104. The imagereconstructor 112 can perform image reconstruction of the digitalsignals and output the reconstructed image data to the computing device113.

The method of FIG. 2 includes defining 202 skin of the subject in theacquired image(s). Continuing the aforementioned example, the computingdevice 113 shown in FIG. 1 may segment each patient from the acquiredimage(s) using a suitable thresholding technique. A thresholdingtechnique may be used for segmentation. Subsequently, a polygon orpolyhedron mesh of the patient may be constructed from the segmenteddataset. In an example, processor(s) 130 and memory 132 of the computingdevice 113 may implement any suitable software for segmenting and meshgeneration. The exterior faces of the elements may define an interfacebetween the ambient air and the patient's skin. The computing device 113may use the processor(s) 130 and memory 132 for implementing these andother techniques for defining the skin of the subject in acquiredimages.

The method of FIG. 2 includes characterizing 204 edge sharpness acrossthe skin of the subject via edge profile measurements. Further, themethod of FIG. 2 includes grouping 206 the edge profile measurements bya radial distance of the edge profiles from an isocenter of the imagingdevice to generate oversampled edge profile measurements. The method ofFIG. 2 includes determining 208 the oversampled edge profiles that are apredetermined amount of in-plane resolution for obtaining an edgegradient profile. Continuing the aforementioned example, processor(s)130 and memory 132 of the computing device 113 may make edge spreadfunction (ESF) measurements across the patient's skin. Subsequently,processor(s) 130 and memory 132 of the computing device 113 may bin theESF measurements by their radial distance from the scanner isocenter toconstruct oversampled ESF measurements. Processor(s) 130 and memory 132of the computing device 113 may subsequently bin the oversampled ESFmeasurements by a fraction of the in-plane pixel size and differentiateto obtain the line spread function (LSF).

The method of FIG. 2 includes determining 210 a quality metric of theacquired one or more images based on the edge gradient profile.Continuing the aforementioned example, processor(s) 130 and memory 132of the computing device 113 may calculate the Fourier transform of theLSF and normalize by the value at zero to obtain a CT spatial resolutionindex (RI) analogous to the modulation transfer function (MTF). The RIsmeasured from patient images may be validated against established MTFsmeasured from an image quality phantom and with the aid of an observerstudy.

In a study, twenty-one clinical multidetector CT (MDCT) datasets wereutilized. The MDCT exams are a subset from a database that was generatedwith local IRB approval and HIPPA compliance. Exams were performed on adual source 256 MDCT scanner (Siemens Definition Flash available fromSiemens Medical Systems, Forscheim Germany) using standard adultclinical MDCT protocols, 120 kV tube potential, and a pitch of 0.8. Theprojection data were used for this study, allowing for multiplereconstructions with various reconstruction techniques. The databasecontained adult CT datasets with body mass indices ranging from normalto overweight. The exams consisted of chest and abdominopelvic caseswith CTDIvol values ranging from 5.53 to 12.45 mGy, dose-length product(DLP) values ranging from 148.94 to 956.90 mGy-cm, and effective mAsvalues ranging from 58 to 184 mAs. The datasets included both males andfemales.

A quality control phantom (e.g., Mercury Phantom V3.0) was scanned usingan adult abdominopelvic protocol, 120 kV tube potential, and a pitch of0.6. The CTDIvol, DLP, and e

ective mAs were 20.16 mGy, 1017.53 mGy-cm, and 300 mAs, respectively. Itis noted that, in the alternative, any other suitable quality controlphantom may be used.

The algorithm for measuring the RI from clinical CT images included thefollowing steps: (1) segmentation of the patient's body from the imageto create a binary volume; (2) generating the polygon or polyhedron meshof the patient; and (3) measuring the ESF across the air-skin interfaceof the patient and calculating the RI from the ESF measurements. Allanalyses were applied across the clinical CT datasets.

The patient's body was segmented from the image using amulti-thresholding technique. The table cushion that patients lay on canpose a problem for segmentation because it often times has a similarHounsfeld unit (HU) to skin. Passing the image through multiplethresholds increases the likelihood of eliminating the cushion from thesegmented volume. Seven thresholds were selected by manually finding theoptimal thresholds from a database of CT images that were unrelated tothis study. The threshold values that were used were −475, −190, −165,−175, −150, −155, −400, and −200. For each threshold value, a binarymask is created where the voxels in the CT dataset that exceed thethreshold are assigned a value of “1” in the binary mask and a value of“0” otherwise. A morphological hole filling operation was subsequentlyapplied to fill in the enclosed lower density regions. The seven binarymasks were added together to create an intensity map. The voxels in theintensity map with a value of “7” were identified as the patient and abinary volume of the patient was created based on these voxels. Valuesless than 7 in the intensity map were identified as the background.Other techniques for segmenting the patient from background objects canbe used for this step. Although, the end result should be a segmentationmask where voxels identified as the patient are assigned a value of 1and voxels identified as the background are assigned a value of 0.

An open source mesh generation toolbox, iso2mesh, was used toreconstruct the patient's body from their CT dataset. Any other suitablesoftware toolbox may be used. The “v2m” function in the toolbox was usedfor this project. It requires the binary volume of the patient alongwith user-defined constants, including the size of the mesh. The size ofthe mesh defines how large the elements are, which determines the numberof ESF measurements that can be made. Increasing the mesh size increasesthe area of the triangular faces of the mesh. A smaller mesh results inmore ESF measurements; however, more ESF measurements increase the timeneeded to run the algorithm because more ESF data are generated. Meshsizes of integer values between two and seven, inclusive, wereinvestigated. A mesh size of three provided the best trade-off betweenoverall computation time and the total amount of data acquired.

The outputs of the v2m function that were used are the coordinates ofthe vertices that makeup the mesh elements and a list of the exteriorfaces of the mesh. Two additional functions, “meshcentroid” and“surfacenorm,” were used to determine the centroid of each face and theunit normal vector to each face, respectively. An example procedure forconstructing the polygon or polyhedron mesh of the patient from their CTdataset is depicted in FIG. 3. Particularly, FIG. 3 illustratesdifferent images representing a sequence of an example method forgenerating the polygon or polyhedron mesh of a patient in accordancewith embodiments of the present disclosure. Referring to FIG. 3, thepatient dataset is segment using a multi-thresholding technique toisolate the patient from the surrounding objects, resulting in a binaryvolume of the patient. The binary volume is input into an open sourcemesh generation toolbox to create a mesh of the patient using polygon orpolyhedron elements.

One side of each face of the mesh contains the ambient air, and theother side contains the patient. As such, the faces of the mesh define aregion of high contrast that outlines an interface between the ambientair and the patient's skin. The circle of maximum diameter that encloseseach face was determined. The pixels in the CT dataset that wereenclosed by the circles were used as the starting points for the ESFmeasurements, which were made across the air-skin interface in adirection normal to the face using a suitable method. The “couplingeffect” between in-plane and out-of-plane spatial resolutions can beinsignificant up to ±15. Therefore, only faces with unit normals within±10 degrees of the x-y plane were used to make ESF measurements to limitthe contamination from adjacent slices. The distance from the center ofthe image to the centroid of each face was stored to group themeasurements by their radial distance from the isocenter. This was doneto account for the radial dependence of the MTF.

The ESF measurements were filtered twice to remove those that werecontaminated. The first filter removes the ESF measurements that passthrough the patient's clothing. A threshold of −925 was determined byfinding the average HU of clothing from a database of unrelated images.Measurements that exceed the clothing threshold on the left (air) tailof the ESF were rejected. The second filter removed measurements thatcross outside of the circular field of view (FOV). This step wasincluded primarily for large patients that were close to or outside ofthe FOV.

The section of the ESF measurements inside the patient's body can besporadic due to the irregularity of HUs inside the body. Proceeding withthe original right tail may ultimately lead to erroneous results in thefrequency domain. To overcome this, the right tail was replaced with acopy of the left tail that has been rotated by 180 degrees. First, for agiven edge profile measurement, the derivative may be calculated.Subsequently, going from left to right, the point where the edge profileinitially begins to increase is identified. All of the points of theedge profile to the left of this location are considered to be the air(left) side of the edge profile and a duplicate of these points wascreated. In a similar manner, the location where the edge profile beginsto level off at the top is identified. The duplicate of the left tailwas rotated by 180 degrees and positioned where the ESF leveled off atthe top. An example of this method is depicted in FIGS. 4A-4C.Particularly, FIGS. 4A-4E illustrate graphs showing an example methodfor reconstructing the right tail of the ESF with the left tail. Theright tail of the ESF is identified by finding the point where the ESFlevels off at the top and taking all of the points to the right. Thisportion of the original ESF measurement is discarded. The left tail ofthe ESF is identified by finding the first point where the ESF initiallybegins to increase and taking all the points to the left. This portionof the original ESF measurement is rotated by 180 degrees and shifted tothe point where the ESF levels off at the top. A Fermi fit is applied tothe reconstructed ESF and the center is identified. The center of theESF is subsequently shifted to the origin. Finally, all of the ESFmeasurements that fall within the same radial bin are grouped togetherto create an oversampled ESF.

A Fermi fit using the Levenberg-Marquardt least squares approach wasapplied to the reconstructed ESFs to center all of the measurementsabout a common point. Shifting all of the ESF measurements to centerthem about the origin and grouping those that are in the same radial binresults in an oversampled ESF. These two steps are illustrated in FIGS.4D and 4E. The oversampled ESFs were binned by 10% of the in-plane pixelsize. The binned ESFs were then conditioned before calculating the RI. Asuitable approach to calculate the MTF was used in this study. Itconsists of differentiating the ESF to get the line-spread function. TheLSF was then Fourier transformed and normalized by the value at zero toacquire the RI. An example procedure for determining the RI from the ESFmeasurements is summarized in FIG. 5, which illustrates a diagram of anexample method for calculating the RI from making an oversampled ESFmeasurement across the patient's skin. The lines are Bresenham lines(Ref 15) crossing the air-skin interface made in directions normal tothe triangular faces. ESF measurements were limited to surface normaldirections within ±10 degrees from the horizontal to preventcontamination from adjacent slices. The ESF is constructed from theintensity values along the Bresenham line and the distances from thecenter of each pixel along the line to the plane defined by thetriangular face. The ESFs are grouped by their radial distance from theisocenter to create an oversampled ESF. The oversampled ESF is binned by10% of the in-plane spatial resolution. The binned ESF is thendifferentiated to obtain the LSF, and the LSF is Fourier transformed toacquire the RI.

As CT image spatial resolution is dependent on both the algorithm andthe kernel used to reconstruct the projection data, each of the clinicaldatasets was reconstructed using both filtered back-projection (FBP) andsinogram affirmed iterative reconstruction (SAFIRE), an iterativereconstructive algorithm developed by Siemens Healthcare. Kernels B20f,B31f, and B45f were used with FBP, and kernels I26f, I31f, and J45f wereused with SAFIRE. The Mercury Phantom has five different cylindricalsections with diameters of 12, 18.5, 23, 30, and 37 cm. Since thelargest section of the Mercury Phantom is 370 mm in diameter, theprojection datasets were reconstructed with a 400 mm field of view. A512×512 matrix was used for each image reconstruction, yielding a pixelsize of 0.78×0.78 mm in the x-y plane. A slice thickness of 0.6 mm wasused.

A three-step validation process was performed to determine how theproposed method compared with the current techniques for measuring theMTF in CT. The first step in the validation includes taking RImeasurements along the surface of the Mercury Phantom. The algorithm wasused to measure the MTF from the exterior surface of the phantom at eachof the four cylindrical sections along its length. The frequencyassociated with 50% MTF (f50) was plotted against the distance from thecenter of the image to the surface of the phantom at each of the foursections to determine if the proposed algorithm could detect the radialdependence of the MTF.

In the second step of the validation process, the MTF and, subsequently,f₅₀ were measured from the air insert inside the 23 cm diameter sectionof the Mercury Phantom. This step includes measuring the ESF across theair-insert/phantom interface, differentiating the ESF and taking theFourier transform, then normalizing by the value at zero to acquire theMTF. The value of f₅₀ for the air inserts spans a range of distances,accounting for the finite diameter of the cylindrical air insert.Similarly, the values of f₅₀ measured from the patient surface span arange of distances accounting for the 10 mm width of the radial bin thatthe ESF measurements were placed in. A linear fit was applied to thedata from the first step and extrapolated back to the f₅₀ measurement ofthe air insert. The linear fit was chosen based on data presented by LaRiviere and Vargas. As the air insert is closer to the isocenter thanthe phantom surfaces, it is expected to produce a larger value for f₅₀than the surface measurements.

Finally, in the third step of the validation, the patient-specific RImeasurements were compared with the Mercury Phantom measurements. Thef₅₀ measurements from patient images were presented in a cloud clusterform around the data from the first two steps of the validationprocedure. Moreover, the f₅₀ measurements from the 160-170 mm radial binwere plotted against Mercury Phantom f₅₀ measurements at 165 mm from theisocenter to investigate the sensitivity of the proposed algorithm tothe reconstruction technique. A linear fit was applied to these data andthe slope and trend of the line were evaluated.

The spread in the f₅₀ measurements in the 160-170 mm radial bin wasquantified. An observer study was conducted to determine if the measureddifferences in spatial frequency for a given kernel across patients werereflective of visually discernable sharpness differences. Seven imagingscientists participated in the study. The observers were shown 72 pairsof edges that were extracted from different images that werereconstructed with the same reconstruction algorithm and kernel. Using atwo-alterative-forced-choice (2AFC) methodology, they were instructed toselect the edge that they perceived to be blurrier. The readings werepreceded with a training read of 64 image pairs. An example picture ofthe graphical user interface created for the observer study, as well assix example edge pairs, is shown in FIG. 6. Particularly, a top ofportion of FIG. 6 shows an example GUI utilized for the study. Thebottom portion of FIG. 6 shows six example sets of edge pairs that wereshown to the observers. Each edge pair consists of two imagesreconstructed with identical reconstruction techniques, but taken fromtwo different clinical datasets.

FIGS. 7A-7F show graphs of results from f₅₀ measurements made fromclinical datasets. Particularly, FIGS. 7A-7F show the results of thefirst two steps in the validation process. FIGS. 7A-7F also show theresults of the f₅₀ measurements from the third step of the validation.FIG. 7A shows SAFIRE with I26f. FIG. 7B shows SAFIRE with I31f. FIG. 7Cshows SAFIRE with J45f. FIG. 7D shows FBP with B20f. FIG. 7E shows FBPwith B31f. FIG. 7F shows FBP with B45f. Mercury phantom is abbreviatedas “MP” and patient-specific is abbreviated as “PS.” The cloud of datapoints around the linear fit line represents the patient specific f₅₀measurements. Each measurement spans a range of radial distances fromthe center corresponding to the length of the radial bin that themeasurements were placed in. Three observations can be made from thedata in FIGS. 7A-7F. The first is that the data exhibit a decreasingtrend, indicating that the patient specific algorithm is capable ofcapturing the radial dependence of the MTF. The second is that the f₅₀measurements vary with the reconstruction kernel used, where the valueof f₅₀ increases with increasing kernel strength. Finally, there is aspread in the f₅₀ measurements about the linear fit lines. This spreadindicates that the spatial resolution varies among different patient CTdatasets. In other words, some images are blurrier than others.Additionally, the spread is larger for stronger kernels.

FIGS. 8A and 8B are plots of f₅₀ measured from patient images versus f₅₀measured from the Mercury Phantom. Referring to FIGS. 8A and 8B, thefigures show results from the sensitivity study of an example method inaccordance with embodiments of the present disclosure. The f₅₀measurements located in the 160-170 mm bin from patient datasets areplotted against the f₅₀ measurements from the Mercury Phantom at 165 mm.A linear fit was applied to the data to establish the sensitivity of theproposed technique. A slope less than one would indicate that theMercury Phantom predicted a higher f₅₀ than was measured in the patientimages, equal to one would indicate that the measured f₅₀ agreed withMercury Phantom predictions, and greater than one would indicate thatthe measured f₅₀ was greater than Mercury Phantom predictions.Furthermore, a positive slope would indicate that the proposed techniquewas sensitive to changes in the reconstruction kernel, and a slope ofzero would indicate that the proposed technique was insensitive to thereconstruction kernel. As shown in FIGS. 8A and 8B, the slope of the fitline is positive and less than one for both reconstruction algorithms.

FIG. 9 shows the results from the observer study. Error bars arereported as ±1 standard deviation to indicate the degree of variabilityin the observers' decisions. The data presented are the percentage ofobservers selecting the blurrier edge for each of the reconstructionalgorithms and reconstruction kernels investigated in this study. Thehighest percentage of observers selecting the blurrier edge was 84.5%corresponding to the B31f kernel used in FBP. Moreover, the lowestpercentage of observers selecting the blurrier edge was 71.4%corresponding to the J45f kernel used in SAFIRE. Overall the percentageof observers selecting the blurrier edge was higher for FBP than forSAFIRE across all reconstruction kernels.

Characterizing spatial resolution is an essential step in quantifyingthe overall quality of CT images, assessing the performance of theimaging system, and optimizing clinical protocols. Conventionaltechniques based on phantom measurements are sufficient incharacterizing the inherent spatial resolution capability of a CTsystem. However, there are limitations in applicability of such measuresto the actual spatial resolution of clinical images. Phantommeasurements are based on static, uniform objects inside the FOV.Clinical images are subject to blurring processes (including patientmotion and scan variability) that are not reflected in idealized phantomimages. This work documents this difference and demonstrates amethodology by which spatial resolution can be measured in actualclinical images. Moreover, this technique accounts for the skin as anorgan, sweat, and contour changes, which can all affect CT imagequality.

The sensitivity study showed that there was a large spread in themeasured spatial frequencies, even when the images were reconstructedwith the same reconstruction algorithm and kernel. This result may becontrary to what is currently accepted. Most researchers and clinicalphysicists follow the idea that spatial resolution in clinical images isdetermined by phantom measurements. That is, they ascribe the MTF aproperty reflective of the imaging system only. The results presentedherein show that the concept of the MTF can be repurposed as areflection of spatial resolution in clinical images. Resolution indices(MTF analogs) measured from clinical images show variations acrossclinical images even when identical image reconstruction parameters areused. The fact that human observers can visually perceive thesevariations substantiates that spatial resolution is a clinicalmeasureable, varying, and relevant metric of clinical image quality.

FIG. 8 exhibits a spread in the f50 measurements, which raises thequestion of how can spatial resolution vary among clinical images thatwere reconstructed with identical reconstruction algorithms and kernels.One avenue in which this is possible is when automatic exposure controlis used. As the tube current varies along the patient, so too does thesize of the effective focal spot due to focal spot blooming. It wasdetermined that focal spot blooming was a source of a portion of thespread in f50, and the spread was reduced by 25% after correcting forfocal spot blooming. Tube potential can also affect the size of theeffective focal spot. However, this was not a source of the spread inFIG. 8, because the same tube potential was used for all of the scans.Another potential source of the spread in the measured f₅₀ values ispatient motion. However, this hypothesis has yet to be proven in thecontext of the present work. Image denoising methods should not have animpact on the results. An investigation in the relationship betweenimage noise and f50 showed that, for a given reconstruction technique,the two values were not correlated.

Vendor proprietary reconstruction techniques, such as sonogramsmoothing, can also produce spatial resolution irregularities acrossclinical datasets, and they are likely an additional cause for thespread in the f₅₀ measurements. Sinogram smoothing techniques areimplemented to reduce noise, but they can lead to spatial resolutionnonuniformities by applying different weighting factors to differentclinical datasets.

One example advantage of the proposed technique is that it allows forthe characterization of image spatial resolution on a patient-specificbasis. It has been shown that clinical images encounter varying amountsof blur, even when they are reconstructed with the same reconstructionalgorithm and kernel. This implies that not all images may have the samespatial resolution that is predicted by phantom measurements. Theproposed technique can be used to measure the degree of variability inimage quality among clinical images and use this information to optimizeclinical protocols in an effort to make image quality consistentthroughout all scanners in the clinic.

Quantifying the spatial resolution characteristics on an image-specificbasis would allow for patient specific image quality tracking. Everypatient that receives a CT scan in the clinic can have information aboutthe image quality of the dataset stored in his or her patient record.This information can then be analyzed along with the acquisitionsettings to determine the optimal settings that balance image qualityand dose for their next scan. In that way, CT dose monitoring can beextended to performance monitoring including image quality attributes ofnoise and now also spatial resolution. This method for characterizing CTspatial resolution has some limitations. Using harder kernels toreconstruct the dataset results in a noisier image set. The higher noisecan make it difficult to extract clean ESFs from the air-skin interface,as the number of individual ESF measurements to produce the oversampledESF is insufficient.

In accordance with embodiments, systems and methods are disclosed formeasuring and presenting image contrast or another imagecharacterization. An example contrast measurement method may be based onmultiple steps. In this example operating on a clinical dataset, firstthe patient's body may be segmented from the dataset to isolate it fromother objects in the field of view (FOV), such as clothing, wires, andother objects. The table cushion may pose a problem for segmentationbecause it often times has a similar HU to skin. Seven thresholds may beidentified based on an independent database of CT images to segment thepatient body from the background. A morphological hole filling operationmay subsequently be applied to fill in low-density regions inside thepatient with HUs outside of the seven thresholds. The result may be abinary volume of the patient, splitting the CT dataset into a foregroundand a background. All subsequent processes may be applied to theforeground dataset.

In accordance with embodiments, FIG. 10 illustrates a flow diagram of anexample method for determining a quality metric of an acquired CT imagein accordance with embodiments of the present disclosure. In thisexample, the method is described as being implemented by the systemshown in FIG. 1, although it should be understood that the method may beimplemented by any other suitable imaging system. Alternative systemsmay be CT imaging systems or non-CT imaging system, such as Mill.

Referring to FIG. 10, the method includes using 1000 an imaging deviceto acquire one or more images including at least a portion of an organof a subject. For example, the system of FIG. 1 may be operated toacquire multiple CT images of a portion of the patient's body 104. Thesystem may direct x-rays towards and through the body 104. The detectorelements of the x-ray detector array 103 may receive the x-raysprojected through the body 104. The DAS 111 can receive analog data fromthe detector elements and can convert the data to digital signalsrepresentative of the body 104. The image reconstructor 112 can performimage reconstruction of the digital signals and output the reconstructedimage data to the computing device 113.

FIG. 10 also includes defining 1002 multiple regions of interest withinthe portion of the organ. FIG. 10 also includes characterizing 1004 theregions of interest based on predetermined criteria. Continuing theaforementioned example, the acquired images may include lungs. Thememory 132 and processor(s) 130 may segment the lungs from the datasetusing Otsu thresholding or another suitable thresholding technique. Thevoxels within the lung section of the histogram define a lung mask and acorresponding lung dataset. The lung dataset contained both lung tissueand lung vasculature. Otsu thresholding was used again so sample lungtissue independent from lung vasculature. A circular mask representing aregion of interest (ROI) was convolved with each slice of the lungtissue mask. The resulting intensity map was used to identify theoptimal locations to sample the lung tissue. The number of ROIscorresponding to maximum intensity values of the intensity map weregradually increased to achieve a sufficient sample distribution. FiveROIs created a histogram with a large number of HU samples. Thehistogram of the ROIs was extracted and further scalarized in terms ofthe mean and standard deviation. FIG. 10 includes presenting 1006 thecharacterization of the regions of interest to a user. For example, thehistogram of the ROIs may be presented via the user interface 115 shownin FIG. 1.

In an example, FIGS. 11A-11C illustrates different images depicting asequence of an example method for segment out the patient's body from aCT dataset in accordance with embodiments of the present disclosure.Referring to FIG. 11A, the method may begin with a CT dataset acquiredfrom a patient. At FIG. 11B, the patient's body may be segmented tocreate a binary mask of the patient. At FIG. 11C, values of −3024 may beassigned to voxels that are not classified as the patient.

For lung histogram measurement, lungs may be segmented from the CTdataset using Otsu thresholding or another suitable thresholdingtechnique. In an example, three thresholds may be used to split thehistogram of each slice of the CT dataset into three sections: one forlung tissue, one for soft tissue, and one for bones. The voxels withinthe lung section of the histogram can define a lung mask and acorresponding lung dataset. The lung dataset may contain both lungtissue and lung vasculature. Otsu thresholding or another suitablethresholding technique may be used so sample lung tissue independentfrom lung vasculature. A circular mask representing a region of interest(ROI) may be convolved with each slice of the lung tissue mask. Theresulting intensity map may be used to identify the optimal locations tosample the lung tissue. The number of ROIs corresponding to maximumintensity values of the intensity map may be gradually increased toachieve a sufficient sample distribution. In this example, five ROIscreated a histogram with a large number of HU samples. The histogram ofthe ROIs may be extracted and further scalarized in terms of the meanand standard deviation.

A flow diagram of an example method for determining the lung HUhistogram in accordance with embodiments of the present disclosure isshown in FIGS. 12A-12E. At FIG. 12A, the method may begin with a CTdataset. At FIG. 12B, the patient's lungs may be segmented out using aglobal Otsu threshold. At FIG. 12C, the vasculature may be removed fromthe lung segmentation, and a binary mask may be created. At FIG. 12D,the slices of the binary mask may be convolved with a binary circle tocreate an intensity map. Subsequently at FIG. 12E, the ROIs may beplaced in the locations of the five maximum intensity values from theintensity map.

For liver histogram measurement, a cylindrical ROI inside the liver issufficient to measure the liver histogram. Since the liver is locateddirectly below the right lung, the centroid of the right lung in theslice where its cross sectional area was at maximum may be identified todetermine the center of the cylindrical ROI. The bottom slice of theright lung may be used as the z-location for the ROI.

The center of the cylindrical ROI was positioned at the (x, y, z)location. Circular ROIs may be placed in the two slices above and belowthe original z location using the same (x, y) coordinates. That is,circular ROIs can be placed in the slices from z−2 to z+2. Thez-location of the centroid of the ROI was optimized to ensure it onlyincludes the liver. This was done by computing the skewness of thehistogram of HUs in the cylindrical ROI, and adjusting the ROI up ordown until the histogram represented a Gaussian distribution. Thehistogram of the ROI may be extracted and further scalarized in terms ofthe mean and standard deviation.

In accordance with embodiments, FIG. 13A-13E illustrates images showinga sequence of a flow diagram of an example method for determining theliver HU histogram. At FIG. 13A, a lung mask may be provided. At FIG.13B, the left lung may be removed from the lung mask. Subsequently atFIG. 13C, the slice with the maximum cross-sectional area of the rightlung may be found. Also, at FIG. 13C, the centroid may be found. At FIG.13D, the last slice of the lung (bottom of the lung) may be found. Nowthe x, y, and z locations of the center of the circular ROIs are known.At FIG. 13E, ROIs are placed in the 5 slices where the liver is known tobe.

Using an IRB-approved protocol, the automated technique was validatedagainst manual segmentation of the organs in 15 non-contrast enhanceddatasets and 15 contrast enhanced datasets. ROIs of similar sizes andshapes that were used in the automated technique were manually placed infive slices of the liver and lung, and the corresponding histograms wereconstructed and scalarized.

The sensitivity of the automated technique was investigated by comparingthe metrics determined from the automated technique to those determinedby manual segmentation. The mean of the HUs inside the automaticallyselected ROIs of the liver and lung were plotted against the mean of theHUs inside the manually selected ROIs. A linear fit was applied to eachdataset to establish the sensitivity of the proposed automated techniqueand the coefficient of determination, R², was computed.

FIGS. 14A and 14B are graphs showing example sets of histograms of theHUs for the various organs determined using the automated technique. Thehistograms shown are for a contrast enhanced exam. The histogramsconstructed from the manual technique are also included for comparison.The histograms of the liver and lung exhibit the expected Gaussiandistribution for both the automated and manual technique. Thesehistograms demonstrate qualitative agreement between the automated andmanual techniques. These histograms were measured from a contrastenhanced chest exam. The histograms were normalized so that the integralwould be equal to one.

FIGS. 15A and 15B are graphs showing the plots of the metrics determinedfrom the automated technique plotted against those determined from themanual technique for non-contrast enhanced exams. The plots aresensitivity plots for non-enhanced chest CT exams. These plots weregenerated from measurements of 15 clinical datasets. A linear fit wasapplied to the data. FIG. 15A shows the liver, and FIG. 15B shows thelung.

Similarly, FIGS. 16A and 16B are graphs showing the plots of the metricsdetermined from the automated technique plotted against those determinedfrom the manual technique for contrast enhanced exams. The slope of thefit line is near unity for all of the plots shown. Moreover, thecoefficient of determination is high for all of the fit lines,indicating a good fit. The plots are sensitivity plots for contrastenhanced chest CT exams. These plots were generated from measurements of15 clinical datasets. A linear fit was applied to the data. FIG. 16Ashows the liver, and FIG. 16B shows the lung.

Overall, the algorithm successfully measured the histograms of the twoorgans in both contrast and non-contrast enhanced chest CT exams for allof the cases examined. The automated measurements were in agreement withmanual measurements. The algorithm exhibits high sensitivity andaccuracy as indicated by the near unity slope of the automated versusmanual measurement plots with high coefficients of determination, R²,values ranging from 0.88 to 0.99.

Computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present subject matter may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present subject matter.

Aspects of the present subject matter are described herein withreference to flowchart illustrations and/or block diagrams of methods,apparatus (systems), and computer program products according toembodiments of the subject matter. It will be understood that each blockof the flowchart illustrations and/or block diagrams, and combinationsof blocks in the flowchart illustrations and/or block diagrams, can beimplemented by computer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present subject matter. In this regard, each block inthe flowchart or block diagrams may represent a module, segment, orportion of instructions, which comprises one or more executableinstructions for implementing the specified logical function(s). In somealternative implementations, the functions noted in the block may occurout of the order noted in the figures. For example, two blocks shown insuccession may, in fact, be executed substantially concurrently, or theblocks may sometimes be executed in the reverse order, depending uponthe functionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

While the embodiments have been described in connection with the variousembodiments of the various figures, it is to be understood that othersimilar embodiments may be used or modifications and additions may bemade to the described embodiment for performing the same functionwithout deviating therefrom. Therefore, the disclosed embodiments shouldnot be limited to any single embodiment, but rather should be construedin breadth and scope in accordance with the appended claims.

1-13. (canceled)
 14. A method comprising: using an imaging device toacquire one or more images including at least a portion of an organ of asubject; defining a plurality of regions of interest within the portionof the organ; characterizing the regions of interest based onpredetermined criteria; and presenting the characterization of theregions of interest to a user.
 15. The method of claim 14, wherein theorgan is one of a lung and liver.
 16. The method of claim 14, whereinthe one or more acquired images are one of contrast enhanced ornon-contrast enhanced images.
 17. The method of claim 14, wherein usingthe imaging device comprises using a computed tomography (CT) imagingdevice to acquire one or more CT images of the at least a portion of theorgan of the subject.
 18. The method of claim 14, defining the pluralityof regions of interest comprises applying a thresholding technique tothe one or more images.
 19. The method of claim 14, further comprisingdefining an area of the at least the portion of the organ of the subjectwithin the one or more images.
 20. The method of claim 19, whereindefining the area comprises using an Otsu thresholding technique tosegment the organ of the subject.
 21. The method of claim 14, whereindefining the area comprises automatically defining the area of the atleast a portion of the organ.
 22. The method of claim 14, whereincharacterizing the regions of interest comprises automaticallycharacterizing statistics of voxel values inside the regions ofinterest.
 23. The method of claim 14, wherein defining the plurality ofregions of interest comprises: generating an intensity map of thepotential regions of interest; and using the intensity map to define theregions of interest.
 24. The method of claim 23, wherein characterizingthe regions of interest comprises generating a histogram based on theintensity map.
 25. The method of claim 24, wherein generating thehistogram comprises generating the histogram based on a number ofHounsfield units within the regions of interest.
 26. The method of claim25, further comprising applying statistics to the histogram to result inthe characterization of the regions of interest. 27-39. (canceled)
 40. Asystem comprising: an imaging device configured to acquire one or moreimages including at least a portion of an organ of a subject; and acomputing device comprising at least one processor and memory that:defines a plurality of regions of interest within the portion of theorgan; characterizes the regions of interest based on predeterminedcriteria; and presents the characterization of the regions of interestto a user.
 41. The system of claim 40, wherein the organ is one of alung and liver.
 42. The system of claim 40, wherein the one or moreacquired images are one of contrast enhanced or non-contrast enhancedimages.
 43. The system of claim 42, wherein using the imaging devicecomprises using a computed tomography (CT) imaging device to acquire oneor more CT images of the at least a portion of the organ of the subject.44. The system of claim 42, defining the plurality of regions ofinterest comprises applying a thresholding technique to the one or moreimages.
 45. The system of claim 42, further comprising defining an areaof the at least the portion of the organ of the subject within the oneor more images.
 46. The system of claim 45, wherein defining the areacomprises using an Otsu thresholding technique to segment the organ ofthe subject.
 47. The system of claim 42, wherein defining the areacomprises automatically defining the area of the at least a portion ofthe organ.
 48. The system of claim 42, wherein characterizing theregions of interest comprises automatically characterizing statistics ofvoxel values inside the regions of interest.
 49. The system of claim 42,wherein defining the plurality of regions of interest comprises:generating an intensity map of the potential regions of interest; andusing the intensity map to define the regions of interest.
 50. Thesystem of claim 49, wherein characterizing the regions of interestcomprises generating a histogram based on the intensity map.
 51. Thesystem of claim 50, wherein generating the histogram comprisesgenerating the histogram based on a number of Hounsfield units withinthe regions of interest.
 52. The system of claim 51, further comprisingapplying statistics to the histogram to result in the characterizationof the regions of interest.